No abstract

No abstract

This study investigates the potential of Fourier transform infrared spectroscopy (FTIR) to monitor glycation-induced changes in protein structure. Aqueous solutions of sodium caseinate and glucose (1:2 w/w, pH 6.7) were heated at 90 degrees C for 0, 10, 20, 40 and 60 min. Evidence for caseinate glycation was obtained by mass spectrometry techniques (electrospray (ESI) and matrix-assisted laser desorption ionisation (MALDI)). FTIR was able to discriminate between glycated and non-glycated sodium caseinate, when the data were analysed by multivariate statistical methods; principal component analysis (PCA) and soft independent modelling of class analogy (SIMCA). The techniques used were complementary and provided different levels of information about the glycated samples.

Infrared (IR) spectra of different varieties of document papers were collected with the use of attenuated total reflectance (ATR, 4000-650 cm−1, eight paper varieties) and diffuse reflectance (DRIFTS, 9000-2500 cm−1, six paper varieties) techniques. The spectral data were classified by the application of soft independent modeling of class analogies (SIMCA), using principal components analysis (PCA) to estimate the distance of separation between the different classes of paper samples and discriminant analysis (DA) to obtain a probabilistic classification. The use of DA on spectral data needed a preliminary data reduction step, either by PCA-decomposition of spectra or the selection of discrete spectral features having maximum discriminating ability. The aim of this research was to evaluate these data-reduction techniques and compare the discriminating power of these two spectral techniques (DRIFTS and ATR) by the application of PCA and DA. The use of PCA scores as DA variables provided the best resolution (100% correct classification) for the DRIFTS spectra, while PCA on the ATR spectra resulted in the best discrimination, separating 67.86% paper pairs completely with the use of cross-validation. The results of this study reemphasize that infrared spectroscopy coupled with multivariate statistical methods of analysis could provide a powerful discriminating tool for the forensic questioned document examiner.

Inks from seven black and eight blue ballpoint pens were separated by a high-performance liquid chromatography (HPLC) method utilizing a photodiode array detection (PDA). A classifier flowchart was designed for the chromatographic data based on the presence or absence of certain peaks at different wavelengths to qualitatively discriminate between the inks. The same data were quantitatively classified by principal components analysis (PCA) to estimate the separation between a pair of classes of ink samples. It was found that the black ballpoint pen inks were discriminated satisfactorily utilizing two-dimensional data of the peak areas and retention times at the optimum wavelengths. The blue pens were discriminated by analyzing the chromatographic data at four different wavelengths simultaneously with a cross-validated PCA. The results of this study indicated that HPLC-PDA coupled with chemometrics could make a powerful discriminating tool for the forensic chemist, especially when analyzing extensive and/or complex data.